Introduction
A mixed model with random effects was chosen for this multifactor experiment, and analyzed using the limma package in R. This package implements a linear modeling approach and empirical Bayes statistics. The limma package with the voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline.
In this model, clade (levels = Clade1, Clade2, Clade3), physiology (levels = Marine, Freshwater), and experimental condition (levels = 15ppt, 0.2ppt) are fixed effects while species (levels = 14) is considered a random effect.
The raw counts file, generated with NumReads from the salmon (version 0.12.0) quantification tool and summarized with the tximport Bioconductor package (version 1.10.1) in R, can be downloaded from an osf repository with this link, then imported into the R framework.
Samples from species with low numbers of replicates were dropped from the raw counts table (F. zebrinus, F. nottii, F. sciadicus). The raw counts table has the following dimensions (genes x samples).
#dim(design)
#[1] 5 81
dim(counts)
[1] 30466 81
Sample Design Matrix
A matrix was generated using the following model with fixed effects:
~0 + physiology*condition*clade
The random effect of species will be taken into account later.
Filtering and Normalization
Genes with low expression across samples were dropped from the analysis using a conservative approach. The function filterByExpr was used on the raw counts matrix. For each condition_physiology group (regardless of species), each sample must have a minium count of 10, and a group minium total count of 100. This reduced the counts table to the following dimensions (genes x samples):
gene.names<-rownames(counts)
counts<-as.matrix(as.data.frame(sapply(counts, as.numeric)))
rownames(counts)<-gene.names
#class(counts)
keep<-filterByExpr(counts,design = design,group=condition_physiology,min.count = 10, min.total.count = 100)
counts.filt <- counts[keep,]
dim(counts.filt)
#write.csv(counts.filt,"../../../21k_counts_filt_30April2019.csv")
Genes of Interest
After filtration, we check the counts matrix for the presence of several genes of interest. These genes have demonstrated responsiveness to salinity change from past studies.
| zymogen granule membrane protein 16 |
Funhe2EKm029929 |
ENSFHEP00000007220.1 |
|
| zymogen granule membrane protein 16 |
Funhe2EKm029931 |
ENSFHEP00000025841 |
|
| solute carrier family 12 member 3-like (removed) |
Funhe2EKm006896 |
ENSFHEP00000009214 |
|
| chloride channel, voltage-sensitive 2 (clcn2), transcript variant X2 (removed) |
Funhe2EKm024148 |
ENSFHEP00000019510 |
|
| ATP-sensitive inward rectifier potassium channel 1 |
Funhe2EKm001965 |
ENSFHEP00000015383 |
|
| inward rectifier potassium channel 2 |
Funhe2EKm023780 |
ENSFHEP00000009753 |
|
| aquaporin-3 |
|
ENSFHEP00000006725 |
|
| cftr |
Funhe2EKm024501 |
ENSFHEP00000008393 |
|
| polyamine-modulated factor 1-like |
Funhe2EKm031049 |
ENSFHEP00000013324 |
|
| sodium/potassium/calcium exchanger 2 |
Funhe2EKm025497 |
ENSFHEP00000034177 |
|
| septin-2B isoform X2 |
|
ENSFHEP00000015765 |
|
| CLOCK-interacting pacemaker-like |
Funhe2EKm026846 |
ENSFHEP00000017303 |
|
| vasopressin V2 receptor-like |
Funhe2EKm026721 |
ENSFHEP00000000036 |
|
| sodium/potassium-transporting ATPase subunit beta-1-interacting protein 1 |
Funhe2EKm001174 |
ENSFHEP00000031108 |
|
| septin-2 |
Funhe2EKm012182 |
ENSFHEP00000016853 |
|
| otopetrin-2 |
Funhe2EKm035427 |
ENSFHEP00000026411 |
|
| claudin 8 |
Funhe2EKm037718 |
ENSFHEP00000006282 |
|
| claudin 4 |
Funhe2EKm007149 |
ENSFHEP00000003908 |
|
If the Ensembl ID displays below, the gene is present in the whole data set and has not been filtered.
# ---------------------------
# Andrew Whitehead's genes of interest:
# ---------------------------
# zymogen granule membrane protein 16
# Funhe2EKm029929
# ENSFHEP00000007220.1
countsfilt <- counts.filt
countsfilt$row <- rownames(countsfilt)
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000007220.1"]
goi
# zymogen granule membrane protein 16
# Funhe2EKm029931
# ENSFHEP00000025841
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000025841"]
goi
# solute carrier family 12 member 3-like (removed)
# Funhe2EKm006896
# ENSFHEP00000009214
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000009214"]
goi
# chloride channel, voltage-sensitive 2 (clcn2), transcript variant X2 (removed)
# Funhe2EKm024148
# ENSFHEP00000019510
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000019510"]
goi
# ATP-sensitive inward rectifier potassium channel 1
# Funhe2EKm001965
# ENSFHEP00000015383
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000015383"]
goi
# inward rectifier potassium channel 2
# Funhe2EKm023780
# ENSFHEP00000009753
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000009753"]
# --------------------------------
# other salinity genes of interest
# --------------------------------
# ============================================
# aquaporin-3
# ENSFHEP00000006725
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000006725"]
goi
# cftr
# Funhe2EKm024501
# ENSFHEP00000008393
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000008393"]
goi
# polyamine-modulated factor 1-like
# Funhe2EKm031049
# ENSFHEP00000013324
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000013324"]
goi
# sodium/potassium/calcium exchanger 2
# ENSFHEP00000034177
# Funhe2EKm025497
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000034177"]
goi
# septin-2B isoform X2
# ENSFHEP00000015765
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000015765"]
goi
# CLOCK-interacting pacemaker-like
# ENSFHEP00000017303
# Funhe2EKm026846
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000017303"]
goi
# vasopressin V2 receptor-like
# Funhe2EKm026721
# ENSFHEP00000000036
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000000036"]
goi
# sodium/potassium-transporting ATPase subunit beta-1-interacting protein 1
# ENSFHEP00000031108
# Funhe2EKm001174
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000031108"]
goi
# septin-2
# Funhe2EKm012182
# ENSFHEP00000016853
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000016853"]
goi
# otopetrin-2
# Funhe2EKm035427
# ENSFHEP00000026411
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000026411"]
goi
# claudin 8
# Funhe2EKm037718
# ENSFHEP00000006282
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000006282"]
goi
# claudin 4
# ENSFHEP00000003908
# Funhe2EKm007149
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000003908"]
goi
all_goi<-c("ENSFHEP00000007220.1","ENSFHEP00000025841","ENSFHEP00000019510",
"ENSFHEP00000015383","ENSFHEP00000009753","ENSFHEP00000006725","ENSFHEP00000008393",
"ENSFHEP00000013324","ENSFHEP00000001609","ENSFHEP00000013324","ENSFHEP00000034177",
"ENSFHEP00000015765","ENSFHEP00000017303","ENSFHEP00000000036","ENSFHEP00000031108",
"ENSFHEP00000016853","ENSFHEP00000003908")
Exploratory Plots
Log counts before normalization:
# log counts before DE
boxplot(log(counts.filt+1), las = 2, main = "")
Log counts after cpm normalization
# ---------------
# DE analysis
# ---------------
genes = DGEList(count = counts.filt, group = condition_physiology_clade)
genes = calcNormFactors( genes )
# write normalized counts
dir <- "~/Documents/UCDavis/Whitehead/"
tmp <- as.data.frame(cpm(genes))
tmp$Ensembl <- rownames(tmp)
tmp <- dplyr::select(tmp, Ensembl, everything())
write.csv(tmp, file = file.path(dir, "normalized_counts.csv"), quote = F, row.names = F)
vobj = voom( genes, design, plot=TRUE)
lcpm <- cpm(genes$counts, log = TRUE)
# log counts after DE
boxplot(lcpm, las = 2, main = "")
plot(colSums(t(lcpm)))
Voom weights:
vwts <- voomWithQualityWeights(genes, design=design, normalization="quantile", plot=TRUE)
The random effects of species are taken into account with the duplicateCorrelation function, which estimates the correlation between species. This reflects the between-species variability. The higher the correlation (0-1.0), the higher the variability between species.
corfit <- duplicateCorrelation(vobj,design,block=ExpDesign$species)
corfit$consensus
#[1] 0.751381
Individuals clustered by overall expression

Individuals clustered by Top 100 genes

Individuals clustered by top 50 gene expression

PCA for overall expression

Contrasts
After running the lmFit function, which fits the linear model for each gene in the matrix and takes the random effects correlation into account, the resulting linear model fit is then used to compute the estimated coefficients and standard errors for a given set of contrasts:
fitRan <- lmFit(vobj,design,block=ExpDesign$species,correlation=corfit$consensus)
#colnames(coef(fitRan))
y <- rnorm(n = nrow(design))
dummy.mod <- lm(y ~ physiology*condition*clade,
data = ExpDesign)
pairs <- pairs(emmeans(dummy.mod, ~condition|clade*physiology ), reverse = T)
contrast.matrix <- pairs@linfct
tmp <- pairs@grid
contrasts <- gsub(" ", "", tmp$contrast)
contrasts <- gsub("-", "_v_", contrasts)
contrasts <- paste0(contrasts, "_", tmp$clade, "_", tmp$physiology)
rownames(contrast.matrix) <- contrasts
contrasts
[1] "15_ppt_v_0.2_ppt_Clade1_FW" "15_ppt_v_0.2_ppt_Clade2_FW" "15_ppt_v_0.2_ppt_Clade3_FW"
[4] "15_ppt_v_0.2_ppt_Clade1_M" "15_ppt_v_0.2_ppt_Clade2_M" "15_ppt_v_0.2_ppt_Clade3_M"
tables <- lapply(contrasts, function(contr){
print(contr)
cm <- contrast.matrix[contr,]
ph <- sapply(strsplit(as.character(contr), "_"), tail, 1)
cl <- sapply(strsplit(as.character(contr), "_"), tail, 2)
tmp <- contrasts.fit(fitRan, contrasts = cm)
tmp <- eBayes(tmp)
tmp2 <- topTable(tmp, n = Inf, sort.by = "P")
tmp3 <- tmp2
tmp3$row <- rownames(tmp3)
tmp3 <- merge(ann,tmp3,by.x = "ensembl_peptide_id", by.y = "row", all = TRUE)
tmp3 <- tmp3[order(tmp3$adj.P.Val),]
filename <- paste0(contr, ".csv")
#write.csv(tmp2, file = file.path(dir, filename),quote = F)
tab <- kable(head(tmp2, 20), digits = 5, row.names = F)
header1 <- 6
names(header1) <- paste0("Top 20 genes for ", contr)
header2 <- 6
names(header2) <- paste0("Number of genes with adjusted P < 0.05 = ", length(which(tmp2$adj.P.Val < 0.05)))
header3 <- 6
names(header3) <- paste0("Output file is ", filename)
tab <- tab %>% add_header_above(header3, align = 'l') %>% add_header_above(header2, align = 'l') %>% add_header_above(header1, align = 'l', font_size = "larger", bold = T)
tab <- tab %>% kable_styling()
return(list(tab, nump = length(which(tmp2$adj.P.Val < 0.05))))
}
)
[1] "15_ppt_v_0.2_ppt_Clade1_FW"
row names of contrasts don't match col names of coefficients
[1] "15_ppt_v_0.2_ppt_Clade2_FW"
row names of contrasts don't match col names of coefficients
[1] "15_ppt_v_0.2_ppt_Clade3_FW"
row names of contrasts don't match col names of coefficients
[1] "15_ppt_v_0.2_ppt_Clade1_M"
row names of contrasts don't match col names of coefficients
[1] "15_ppt_v_0.2_ppt_Clade2_M"
row names of contrasts don't match col names of coefficients
[1] "15_ppt_v_0.2_ppt_Clade3_M"
row names of contrasts don't match col names of coefficients
Three-way contrasts
The number of genes significant for the three-way interaction of condition:physiology:clade:
sigps <- unlist(lapply(tables, function(x)x[[2]]))
sumtab <- data.frame(Comparison = contrasts, `Number of genes with adjusted P < 0.05` = sigps,
check.names = F)
kable(sumtab, align = 'c') %>% kable_styling() %>%
add_header_above(c("Overview of results" = 2), font_size = "larger", bold = T, align = "l")
Two-way contrasts
These genes demonstrate a conserved response to experimental condition across M or FW physiologies, regardless of clade.
y <- rnorm(n = nrow(design))
dummy.mod <- lm(y ~ physiology*condition*clade,
data = ExpDesign)
pairs <- pairs(emmeans(dummy.mod, ~condition|physiology ), reverse = T)
contrast.matrix <- pairs@linfct
tmp <- pairs@grid
contrasts <- gsub(" ", "", tmp$contrast)
contrasts <- gsub("-", "_v_", contrasts)
contrasts <- paste0(contrasts, "_", tmp$physiology)
rownames(contrast.matrix) <- contrasts
contrasts
tables <- lapply(contrasts, function(contr){
#print(contr)
cm <- contrast.matrix[contr,]
ph <- sapply(strsplit(as.character(contr), "_"), tail, 1)
cl <- sapply(strsplit(as.character(contr), "_"), tail, 2)
tmp <- contrasts.fit(fitRan, contrasts = cm)
tmp <- eBayes(tmp)
tmp2 <- topTable(tmp, n = Inf, sort.by = "P")
#tmp3 <- tmp2
#tmp3$row <- rownames(tmp3)
#tmp3 <- merge(ann,tmp3,by.x = "ensembl_peptide_id", by.y = "row", all = TRUE)
#tmp3 <- tmp3[order(tmp3$adj.P.Val),]
filename <- paste0(contr, ".csv")
#write.csv(tmp2, file = file.path(dir, filename),quote = F)
tab <- kable(head(tmp2, 20), digits = 5, row.names = F)
header1 <- 6
names(header1) <- paste0("Top 20 genes for ", contr)
header2 <- 6
names(header2) <- paste0("Number of genes with adjusted P < 0.05 = ", length(which(tmp2$adj.P.Val < 0.05)))
header3 <- 6
names(header3) <- paste0("Output file is ", filename)
tab <- tab %>% add_header_above(header3, align = 'l') %>% add_header_above(header2, align = 'l') %>% add_header_above(header1, align = 'l', font_size = "larger", bold = T)
tab <- tab %>% kable_styling()
return(list(tab, nump = length(which(tmp2$adj.P.Val < 0.05))))
}
)
The number of genes significant for the two-way interaction of condition:physiology, independent of clade:
sigps <- unlist(lapply(tables, function(x)x[[2]]))
sumtab <- data.frame(Comparison = contrasts, `Number of genes with adjusted P < 0.05` = sigps,
check.names = F)
kable(sumtab, align = 'c') %>% kable_styling() %>%
add_header_above(c("Overview of results" = 2), font_size = "larger", bold = T, align = "l")
Condition - main effect
These genes demonstrate a main effect of condition (15 ppt vs. 0.2 ppt), regardless of clade or physiology.
y <- rnorm(n = nrow(design))
dummy.mod <- lm(y ~ physiology*condition*clade,
data = ExpDesign)
pairs <- pairs(emmeans(dummy.mod, ~condition), reverse = T)
contrast.matrix <- pairs@linfct
tmp <- pairs@grid
contrasts <- gsub(" ", "", tmp$contrast)
contrasts <- gsub("-", "_v_", contrasts)
rownames(contrast.matrix) <- contrasts
contrasts
tables <- lapply(contrasts, function(contr){
#print(contr)
cm <- contrast.matrix[contr,]
ph <- sapply(strsplit(as.character(contr), "_"), tail, 1)
cl <- sapply(strsplit(as.character(contr), "_"), tail, 2)
tmp <- contrasts.fit(fitRan, contrasts = cm)
tmp <- eBayes(tmp)
tmp2 <- topTable(tmp, n = Inf, sort.by = "P")
#tmp3 <- tmp2
#tmp3$row <- rownames(tmp3)
#tmp3 <- merge(ann,tmp3,by.x = "ensembl_peptide_id", by.y = "row", all = TRUE)
#tmp3 <- tmp3[order(tmp3$adj.P.Val),]
filename <- paste0(contr, ".csv")
#write.csv(tmp2, file = file.path(dir, filename),quote = F)
tab <- kable(head(tmp2, 20), digits = 5, row.names = F)
header1 <- 6
names(header1) <- paste0("Top 20 genes for ", contr)
header2 <- 6
names(header2) <- paste0("Number of genes with adjusted P < 0.05 = ", length(which(tmp2$adj.P.Val < 0.05)))
header3 <- 6
names(header3) <- paste0("Output file is ", filename)
tab <- tab %>% add_header_above(header3, align = 'l') %>% add_header_above(header2, align = 'l') %>% add_header_above(header1, align = 'l', font_size = "larger", bold = T)
tab <- tab %>% kable_styling()
return(list(tab, nump = length(which(tmp2$adj.P.Val < 0.05))))
}
)
The number of genes significant for the main effect condition:
sigps <- unlist(lapply(tables, function(x)x[[2]]))
sumtab <- data.frame(Comparison = contrasts, `Number of genes with adjusted P < 0.05` = sigps,
check.names = F)
kable(sumtab, align = 'c') %>% kable_styling() %>%
add_header_above(c("Overview of results" = 2), font_size = "larger", bold = T, align = "l")
Heatmaps
Marine-Clade 1 (three-way) response
rld <- log2(mean_norm_counts_ordered_M_Clade1_sig+1)
geneDists <- dist(mean_norm_counts_ordered_M_Clade1_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
Freshwater-Clade 1 (three-way) response
rld <- log2(mean_norm_counts_ordered_FW_Clade1_sig+1)
geneDists <- dist(mean_norm_counts_ordered_FW_Clade1_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
Marine-Clade 2 (three-way) response
rld <- log2(mean_norm_counts_ordered_M_Clade2_sig+1)
geneDists <- dist(mean_norm_counts_ordered_M_Clade2_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
Freshwater-Clade 2 (three-way) response
rld <- log2(mean_norm_counts_ordered_FW_Clade2_sig+1)
geneDists <- dist(mean_norm_counts_ordered_FW_Clade2_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
Marine-Clade 3 (three-way) response
rld <- log2(mean_norm_counts_ordered_M_Clade3_sig+1)
geneDists <- dist(mean_norm_counts_ordered_M_Clade3_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
Freshwater-Clade 3 (three-way) response
rld <- log2(mean_norm_counts_ordered_FW_Clade3_sig+1)
geneDists <- dist(mean_norm_counts_ordered_FW_Clade3_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
Marine physiology (two-way) conserved response
rld <- log2(mean_norm_counts_ordered_M_sig+1)
geneDists <- dist(mean_norm_counts_ordered_M_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
Freshwater physiology (two-way) conserved response
rld <- log2(mean_norm_counts_ordered_FW_sig+1)
geneDists <- dist(mean_norm_counts_ordered_FW_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
Condition response (main effect)
rld <- log2(mean_norm_counts_ordered_condition_sig+1)
geneDists <- dist(mean_norm_counts_ordered_condition_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
sessionInfo()
---
title: "DEanalysis_kfish_osmotic"
author: "Lisa Johnson"
date: '`r Sys.Date()`'
output:
  html_document:
    code_folding: hide
    collapsed: no
    df_print: paged
    number_sections: yes
    theme: cerulean
    toc: yes
    toc_depth: 5
    toc_float: yes
  html_notebook:
    toc: yes
    toc_depth: 5
---

# Introduction

A mixed model with random effects was chosen for this multifactor experiment, and analyzed using the `limma` package in R. This package implements a linear modeling approach and empirical Bayes statistics. The `limma` package with the `voom` method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline.

In this model, `clade` (levels = Clade1, Clade2, Clade3), `physiology` (levels = Marine, Freshwater), and experimental `condition` (levels = 15ppt, 0.2ppt) are fixed effects while `species` (levels = 14) is considered a random effect.

```{r LoadPackages, results='hide', include=FALSE, warning=FALSE}

# Install function for packages    
packages<-function(x){
  x<-as.character(match.call()[[2]])
  if (!require(x,character.only=TRUE)){
    install.packages(pkgs=x,repos="http://cran.r-project.org")
    require(x,character.only=TRUE)
  }
}

bioconductors <- function(x){
    x<- as.character(match.call()[[2]])
    if (!require(x, character.only = TRUE)){
      source("https://bioconductor.org/biocLite.R")
      biocLite(pkgs=x)
      require(x, character.only = TRUE)
    }
}

packages(MASS)
packages(ggplot2)
packages(gtools)
packages(pheatmap)
packages(cowplot)
packages(RColorBrewer)
packages(dplyr)
packages(tidyr)
packages(knitr)
packages(ggrepel)
bioconductors(DESeq2)
bioconductors(limma)
bioconductors('edgeR')
packages(gplots)
packages(lattice)
packages("vsn")
bioconductors(biomaRt)
packages(kableExtra)
packages(pheatmap)
packages("SummarizedExperiment")
packages("emmeans")
packages(data.table)
```

The raw counts file, generated with `NumReads` from the salmon (version 0.12.0) quantification tool and summarized with the tximport Bioconductor package (version 1.10.1) in R, can be downloaded from an [osf repository](https://osf.io/m4xeg/) with this [link](https://osf.io/7vp38/download), then imported into the R framework.

```{r loadfiles, results='hide', include=FALSE, warning=FALSE}
# This is the counts with Experimental Design Info in the last 5 rows

setwd("~/Documents/UCDavis/Whitehead/RNAseq_15killifish/DE_scripts/limma")
if(!file.exists('~/Documents/UCDavis/Whitehead/Ensembl_species_counts_designfactors.csv')){
  download.file("https://osf.io/7vp38/download",'Ensembl_species_counts_designfactors.csv')
}

counts_design <- read.csv("~/Documents/UCDavis/Whitehead/Ensembl_species_counts_designfactors.csv",stringsAsFactors = FALSE)

```

Samples from species with low numbers of replicates were dropped from the raw counts table (*F. zebrinus*, *F. nottii*, *F. sciadicus*). The raw counts table has the following dimensions (genes x samples). 

```{r dropsamples,results='show', warning=FALSE}

#dim(counts_design)
#[1] 30471   130

# -----------------------
# Format design and counts matrix
# Drop columns with no data
# -----------------------

design <- counts_design[counts_design$Ensembl == 'Empty',]
#design$type <- c("species","native_salinity","clade","group","condition")
drops <- c("X","Ensembl",
           "F_zebrinus_BW_1.quant","F_zebrinus_BW_2.quant",
           "F_zebrinus_FW_1.quant","F_zebrinus_FW_2.quant",
           "F_notti_FW_1.quant","F_notti_FW_2.quant",
           "F_sciadicus_BW_1.quant","F_sciadicus_FW_1.quant","F_sciadicus_FW_2.quant")
transfer_drops <- c("F_sciadicus_transfer_1.quant","F_rathbuni_transfer_1.quant","F_rathbuni_transfer_2.quant","F_rathbuni_transfer_3.quant",
                    "F_grandis_transfer_1.quant","F_grandis_transfer_2.quant","F_grandis_transfer_3.quant",
                    "F_notatus_transfer_1.quant","F_notatus_transfer_2.quant","F_notatus_transfer_3.quant",
                    "F_parvapinis_transfer_1.quant","F_parvapinis_transfer_2.quant",
                    "L_goodei_transfer_1.quant","L_goodei_transfer_2.quant","L_goodei_transfer_3.quant",
                    "F_olivaceous_transfer_1.quant","F_olivaceous_transfer_2.quant",
                    "L_parva_transfer_1.quant","L_parva_transfer_2.quant","L_parva_transfer_3.quant",
                    "F_heteroclitusMDPP_transfer_1.quant","F_heteroclitusMDPP_transfer_2.quant","F_heteroclitusMDPP_transfer_3.quant",
                    "F_similis_transfer_1.quant","F_similis_transfer_2.quant","F_similis_transfer_3.quant",
                    "F_diaphanus_transfer_1.quant","F_diaphanus_transfer_2.quant",
                    "F_chrysotus_transfer_1.quant","F_chrysotus_transfer_2.quant",
                    "A_xenica_transfer_1.quant","A_xenica_transfer_2.quant","A_xenica_transfer_3.quant" ,
                    "F_catanatus_transfer_1.quant","F_catanatus_transfer_2.quant",
                    "F_heteroclitusMDPL_transfer_1.quant","F_heteroclitusMDPL_transfer_2.quant","F_heteroclitusMDPL_transfer_3.quant")
counts<-counts_design[!counts_design$Ensembl == 'Empty',]
rownames(counts)<-counts$Ensembl
design <- design[ , !(names(design) %in% drops)]
counts <- counts[ , !(names(counts) %in% drops)]
design <- design[ , !(names(design) %in% transfer_drops)]
counts <- counts[ , !(names(counts) %in% transfer_drops)]
#dim(design)
#[1]  5 81
dim(counts)
gene.names<-rownames(counts)
design[] <- lapply( design, factor)
```

# Sample Design Matrix

A matrix was generated using the following model with fixed effects: 

```
~0 + physiology*condition*clade
```
The random effect of `species` will be taken into account later.

```{r designinfo,results='show', warning=FALSE}

# --------------------
# design cateogories
# --------------------

species<-as.character(unlist(design[1,]))
physiology<-as.character(unlist(design[2,]))
clade<-as.character(unlist(design[3,]))
condition<-as.character(unlist(design[5,]))
condition_physiology<-as.vector(paste(condition,physiology,sep="."))
condition_physiology_clade <- as.vector(paste(condition_physiology,clade,sep="."))
condition_physiology_clade <- as.vector(paste("group",condition_physiology_clade,sep=""))
cols<-colnames(counts)
ExpDesign <- data.frame(row.names=cols,
                        condition=condition,
                        physiology = physiology,
                        clade = clade,
                        species = species,
                        sample=cols)
ExpDesign
design = model.matrix( ~0 + physiology*condition*clade, ExpDesign)
colnames(design)
# check rank of matrix
#Matrix::rankMatrix( design )
#dim(design)
```

# Filtering and Normalization

Genes with low expression across samples were dropped from the analysis using a conservative approach. The function `filterByExpr` was used on the raw counts matrix. For each `condition_physiology` group (regardless of species), each sample must have a minium count of 10, and a group minium total count of 100. This reduced the counts table to the following dimensions (genes x samples):

```{r filt, results="show",warning=FALSE}

gene.names<-rownames(counts)
counts<-as.matrix(as.data.frame(sapply(counts, as.numeric)))
rownames(counts)<-gene.names
#class(counts)

keep<-filterByExpr(counts,design = design,group=condition_physiology,min.count = 10, min.total.count = 100)
counts.filt <- counts[keep,]
dim(counts.filt)
#write.csv(counts.filt,"../../../21k_counts_filt_30April2019.csv")
```

# Genes of Interest

After filtration, we check the counts matrix for the presence of several genes of interest. These genes have demonstrated responsiveness to salinity change from past studies.

| gene  | Funhe  | Ensembl  |  
|---|---|---|---|---|
| zymogen granule membrane protein 16  | Funhe2EKm029929  | ENSFHEP00000007220.1  |  
| zymogen granule membrane protein 16  | Funhe2EKm029931 | ENSFHEP00000025841  | 
| solute carrier family 12 member 3-like (removed)  | Funhe2EKm006896  |  ENSFHEP00000009214 | 
| chloride channel, voltage-sensitive 2 (clcn2), transcript variant X2 (removed)  | Funhe2EKm024148  |  ENSFHEP00000019510 | 
| ATP-sensitive inward rectifier potassium channel 1 |  Funhe2EKm001965 | ENSFHEP00000015383  | 
| inward rectifier potassium channel 2  |  Funhe2EKm023780 | ENSFHEP00000009753  | 
| aquaporin-3  |   | ENSFHEP00000006725  | 
| cftr  | Funhe2EKm024501  | ENSFHEP00000008393  | 
| polyamine-modulated factor 1-like | Funhe2EKm031049  | ENSFHEP00000013324  | 
| sodium/potassium/calcium exchanger 2  | Funhe2EKm025497 | ENSFHEP00000034177 | 
| septin-2B isoform X2  |   | ENSFHEP00000015765  | 
| CLOCK-interacting pacemaker-like  | Funhe2EKm026846  | ENSFHEP00000017303  | 
| vasopressin V2 receptor-like  | Funhe2EKm026721  | ENSFHEP00000000036 | 
| sodium/potassium-transporting ATPase subunit beta-1-interacting protein 1  | Funhe2EKm001174  | ENSFHEP00000031108  | 
| septin-2  | Funhe2EKm012182  | ENSFHEP00000016853  | 
| otopetrin-2  | Funhe2EKm035427  | ENSFHEP00000026411  | 
| claudin 8  | Funhe2EKm037718  | ENSFHEP00000006282  | 
| claudin 4  | Funhe2EKm007149  | ENSFHEP00000003908  | 

If the Ensembl ID displays below, the gene is present in the whole data set and has not been filtered.

```{r goi, results="show",warning=FALSE}

# ---------------------------
# Andrew Whitehead's genes of interest:
# ---------------------------

# zymogen granule membrane protein 16
# Funhe2EKm029929
# ENSFHEP00000007220.1
countsfilt <- counts.filt
countsfilt$row <- rownames(countsfilt)
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000007220.1"]
goi
# zymogen granule membrane protein 16
# Funhe2EKm029931
# ENSFHEP00000025841
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000025841"]
goi
# solute carrier family 12 member 3-like (removed) 
# Funhe2EKm006896
# ENSFHEP00000009214
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000009214"]
goi
# chloride channel, voltage-sensitive 2 (clcn2), transcript variant X2 (removed)
# Funhe2EKm024148
# ENSFHEP00000019510
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000019510"]
goi
# ATP-sensitive inward rectifier potassium channel 1 
# Funhe2EKm001965
# ENSFHEP00000015383
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000015383"]
goi
# inward rectifier potassium channel 2
# Funhe2EKm023780
# ENSFHEP00000009753
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000009753"]

# --------------------------------
# other salinity genes of interest
# --------------------------------
# ============================================
# aquaporin-3
# ENSFHEP00000006725
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000006725"]
goi
# cftr
# Funhe2EKm024501
# ENSFHEP00000008393
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000008393"]
goi
# polyamine-modulated factor 1-like
# Funhe2EKm031049
# ENSFHEP00000013324
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000013324"]
goi
# sodium/potassium/calcium exchanger 2
# ENSFHEP00000034177
# Funhe2EKm025497
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000034177"]
goi
# septin-2B isoform X2
# ENSFHEP00000015765
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000015765"]
goi
# CLOCK-interacting pacemaker-like
# ENSFHEP00000017303
# Funhe2EKm026846
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000017303"]
goi
# vasopressin V2 receptor-like
# Funhe2EKm026721
# ENSFHEP00000000036
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000000036"]
goi
# sodium/potassium-transporting ATPase subunit beta-1-interacting protein 1
# ENSFHEP00000031108
# Funhe2EKm001174
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000031108"]
goi
# septin-2
# Funhe2EKm012182
# ENSFHEP00000016853
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000016853"]
goi
# otopetrin-2
# Funhe2EKm035427
# ENSFHEP00000026411
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000026411"]
goi
# claudin 8
# Funhe2EKm037718
# ENSFHEP00000006282
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000006282"]
goi
# claudin 4
# ENSFHEP00000003908
# Funhe2EKm007149
goi <- countsfilt$row[countsfilt$row == "ENSFHEP00000003908"]
goi
all_goi<-c("ENSFHEP00000007220.1","ENSFHEP00000025841","ENSFHEP00000019510",
           "ENSFHEP00000015383","ENSFHEP00000009753","ENSFHEP00000006725","ENSFHEP00000008393",
           "ENSFHEP00000013324","ENSFHEP00000001609","ENSFHEP00000013324","ENSFHEP00000034177",
           "ENSFHEP00000015765","ENSFHEP00000017303","ENSFHEP00000000036","ENSFHEP00000031108",
           "ENSFHEP00000016853","ENSFHEP00000003908")
```

# Exploratory Plots

Log counts before normalization:
```{r raw, results="show", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'), warning=FALSE}

# log counts before DE
boxplot(log(counts.filt+1), las = 2, main = "")

```

Log counts after cpm normalization
```{r norm, results="show", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'), warning=FALSE}
# ---------------

# DE analysis

# ---------------

genes = DGEList(count = counts.filt, group = condition_physiology_clade)
genes = calcNormFactors( genes )

# write normalized counts
dir <- "~/Documents/UCDavis/Whitehead/"
tmp <- as.data.frame(cpm(genes))
tmp$Ensembl <- rownames(tmp)
tmp <- dplyr::select(tmp, Ensembl, everything())
write.csv(tmp, file = file.path(dir, "normalized_counts.csv"), quote = F, row.names = F)

vobj = voom( genes, design, plot=TRUE)
lcpm <- cpm(genes$counts, log = TRUE)

# log counts after DE

boxplot(lcpm, las = 2, main = "")
plot(colSums(t(lcpm)))
```

Voom weights:

```{r voom, results="show", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE }

vwts <- voomWithQualityWeights(genes, design=design, normalization="quantile", plot=TRUE)
```

The random effects of `species` are taken into account with the `duplicateCorrelation` function, which estimates the correlation between species. This reflects the between-species variability. The higher the correlation (0-1.0), the higher the variability between species.

```{r randomeffects, results="show", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE }
corfit <- duplicateCorrelation(vobj,design,block=ExpDesign$species)

corfit$consensus
#[1] 0.751381
```

### Individuals clustered by overall expression

```{r PlainHeatmap, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
counts_round<-round(counts.filt,digits=0)
dds <- DESeqDataSetFromMatrix(countData = counts_round,colData = ExpDesign,design = design)
rld <- vst(dds, blind = FALSE,fitType='local')
sampleDists <- dist(t(assay(rld)))
df <- as.data.frame(colData(dds)[,c("physiology","condition","clade")])
sampleDistMatrix <- as.matrix( sampleDists )
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
         clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists,
         col = colors, annotation = df, show_rownames=F)
```

### Individuals clustered by Top 100 genes

```{r MiniPlainGeneHeatmap, echo=FALSE, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
select100 <- order(rowMeans(counts(dds,normalized=FALSE)),decreasing=TRUE)[1:100]
sampleDists <- dist(t(assay(rld)[select100,]))
pheatmap(assay(rld)[select100,], show_rownames=TRUE,clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists, annotation_col=df)
```

### Individuals clustered by top 50 gene expression


```{r MiniPlainHeatmap, echo=FALSE, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}

select50 <- order(rowMeans(counts(dds,normalized=FALSE)),decreasing=TRUE)[1:50]

sampleDists <- dist(t(assay(rld)[select50,]))
sampleDistMatrix <- as.matrix( sampleDists )

pheatmap(sampleDistMatrix, show_rownames=T,clustering_distance_rows = sampleDists,
         clustering_distance_cols = sampleDists, annotation_col=df)
```

### PCA for overall expression

```{r plainPCA, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}

cowplot::plot_grid( plotPCA(rld, intgroup="condition"),
                    plotPCA(rld, intgroup="physiology"),
                    plotPCA(rld, intgroup="clade"),
                    plotPCA(rld, intgroup=c("clade","physiology","condition")),
                           align="c", ncol=2)
```

# Contrasts

After running the `lmFit` function, which fits the linear model for each gene in the matrix and takes the random effects correlation into account, the resulting linear model fit is then used to compute the estimated coefficients and standard errors for a given set of contrasts:

```{r lmfit, results="show", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE }
fitRan <- lmFit(vobj,design,block=ExpDesign$species,correlation=corfit$consensus)
#colnames(coef(fitRan))
y <- rnorm(n = nrow(design))
dummy.mod <- lm(y ~ physiology*condition*clade, 
                data = ExpDesign)
pairs <- pairs(emmeans(dummy.mod, ~condition|clade*physiology ), reverse = T)
contrast.matrix <- pairs@linfct
tmp <- pairs@grid
contrasts <- gsub(" ", "", tmp$contrast)
contrasts <- gsub("-", "_v_", contrasts)
contrasts <- paste0(contrasts, "_", tmp$clade, "_", tmp$physiology)
rownames(contrast.matrix) <- contrasts

contrasts
```


```{r contrasts1, results="show",warning=FALSE}
tables <- lapply(contrasts, function(contr){
    #print(contr)
    cm <- contrast.matrix[contr,]
    ph <- sapply(strsplit(as.character(contr), "_"), tail, 1)
    cl <- sapply(strsplit(as.character(contr), "_"), tail, 2)
    tmp <- contrasts.fit(fitRan, contrasts = cm)
    tmp <- eBayes(tmp)
    tmp2 <- topTable(tmp, n = Inf, sort.by = "P")
    #tmp3 <- tmp2
    #tmp3$row <- rownames(tmp3)
    #tmp3 <- merge(ann,tmp3,by.x = "ensembl_peptide_id", by.y = "row", all = TRUE)
    #tmp3 <- tmp3[order(tmp3$adj.P.Val),]
    filename <- paste0(contr, ".csv")
    #write.csv(tmp2, file = file.path(dir, filename),quote = F)
    tab <- kable(head(tmp2, 20), digits = 5, row.names = F)
    header1 <- 6
    names(header1) <- paste0("Top 20 genes for ", contr)
    header2 <- 6
    names(header2) <- paste0("Number of genes with adjusted P < 0.05 = ", length(which(tmp2$adj.P.Val < 0.05)))
    header3 <- 6
    names(header3) <- paste0("Output file is ", filename)
    tab <- tab %>% add_header_above(header3, align = 'l') %>% add_header_above(header2, align = 'l') %>% add_header_above(header1, align = 'l', font_size = "larger", bold = T)
    tab <- tab %>% kable_styling()
    return(list(tab, nump = length(which(tmp2$adj.P.Val < 0.05))))
}
)

```

# Three-way contrasts

The number of genes significant for the three-way interaction of `condition:physiology:clade`: 

```{r threeway, results="show",warning=FALSE}
sigps <- unlist(lapply(tables, function(x)x[[2]]))
sumtab <- data.frame(Comparison = contrasts, `Number of genes with adjusted P < 0.05` = sigps,
                     check.names = F)
kable(sumtab, align = 'c') %>% kable_styling() %>%
  add_header_above(c("Overview of results" = 2), font_size = "larger", bold = T, align = "l")
```

# Two-way contrasts

These genes demonstrate a conserved response to experimental condition across M or FW physiologies, regardless of clade. 

```{r twoway, results="show",warning=FALSE}
y <- rnorm(n = nrow(design))
dummy.mod <- lm(y ~ physiology*condition*clade, 
                data = ExpDesign)
pairs <- pairs(emmeans(dummy.mod, ~condition|physiology ), reverse = T)
contrast.matrix <- pairs@linfct
tmp <- pairs@grid
contrasts <- gsub(" ", "", tmp$contrast)
contrasts <- gsub("-", "_v_", contrasts)
contrasts <- paste0(contrasts, "_", tmp$physiology)
rownames(contrast.matrix) <- contrasts

contrasts

tables <- lapply(contrasts, function(contr){
  #print(contr)
  cm <- contrast.matrix[contr,]
  ph <- sapply(strsplit(as.character(contr), "_"), tail, 1)
  cl <- sapply(strsplit(as.character(contr), "_"), tail, 2)
  tmp <- contrasts.fit(fitRan, contrasts = cm)
  tmp <- eBayes(tmp)
  tmp2 <- topTable(tmp, n = Inf, sort.by = "P")
  #tmp3 <- tmp2
  #tmp3$row <- rownames(tmp3)
  #tmp3 <- merge(ann,tmp3,by.x = "ensembl_peptide_id", by.y = "row", all = TRUE)
  #tmp3 <- tmp3[order(tmp3$adj.P.Val),]
  filename <- paste0(contr, ".csv")
  #write.csv(tmp2, file = file.path(dir, filename),quote = F)
  tab <- kable(head(tmp2, 20), digits = 5, row.names = F)
  header1 <- 6
  names(header1) <- paste0("Top 20 genes for ", contr)
  header2 <- 6
  names(header2) <- paste0("Number of genes with adjusted P < 0.05 = ", length(which(tmp2$adj.P.Val < 0.05)))
  header3 <- 6
  names(header3) <- paste0("Output file is ", filename)
  tab <- tab %>% add_header_above(header3, align = 'l') %>% add_header_above(header2, align = 'l') %>% add_header_above(header1, align = 'l', font_size = "larger", bold = T)
  tab <- tab %>% kable_styling()
  return(list(tab, nump = length(which(tmp2$adj.P.Val < 0.05))))
}

)
```

The number of genes significant for the two-way interaction of `condition:physiology`, independent of clade: 

```{r twowayresults, results="show",warning=FALSE}
sigps <- unlist(lapply(tables, function(x)x[[2]]))
sumtab <- data.frame(Comparison = contrasts, `Number of genes with adjusted P < 0.05` = sigps,
                     check.names = F)
kable(sumtab, align = 'c') %>% kable_styling() %>%
  add_header_above(c("Overview of results" = 2), font_size = "larger", bold = T, align = "l")
```

# Condition - main effect

These genes demonstrate a main effect of condition (15 ppt vs. 0.2 ppt), regardless of clade or physiology.

```{r conditionmain, results="show",warning=FALSE}
y <- rnorm(n = nrow(design))
dummy.mod <- lm(y ~ physiology*condition*clade, 
                data = ExpDesign)
pairs <- pairs(emmeans(dummy.mod, ~condition), reverse = T)
contrast.matrix <- pairs@linfct
tmp <- pairs@grid
contrasts <- gsub(" ", "", tmp$contrast)
contrasts <- gsub("-", "_v_", contrasts)
rownames(contrast.matrix) <- contrasts

contrasts

tables <- lapply(contrasts, function(contr){
  #print(contr)
  cm <- contrast.matrix[contr,]
  ph <- sapply(strsplit(as.character(contr), "_"), tail, 1)
  cl <- sapply(strsplit(as.character(contr), "_"), tail, 2)
  tmp <- contrasts.fit(fitRan, contrasts = cm)
  tmp <- eBayes(tmp)
  tmp2 <- topTable(tmp, n = Inf, sort.by = "P")
  #tmp3 <- tmp2
  #tmp3$row <- rownames(tmp3)
  #tmp3 <- merge(ann,tmp3,by.x = "ensembl_peptide_id", by.y = "row", all = TRUE)
  #tmp3 <- tmp3[order(tmp3$adj.P.Val),]
  filename <- paste0(contr, ".csv")
  #write.csv(tmp2, file = file.path(dir, filename),quote = F)
  tab <- kable(head(tmp2, 20), digits = 5, row.names = F)
  header1 <- 6
  names(header1) <- paste0("Top 20 genes for ", contr)
  header2 <- 6
  names(header2) <- paste0("Number of genes with adjusted P < 0.05 = ", length(which(tmp2$adj.P.Val < 0.05)))
  header3 <- 6
  names(header3) <- paste0("Output file is ", filename)
  tab <- tab %>% add_header_above(header3, align = 'l') %>% add_header_above(header2, align = 'l') %>% add_header_above(header1, align = 'l', font_size = "larger", bold = T)
  tab <- tab %>% kable_styling()
  return(list(tab, nump = length(which(tmp2$adj.P.Val < 0.05))))
}

)
```

The number of genes significant for the main effect `condition`: 

```{r conditionresults, results="show",warning=FALSE}
sigps <- unlist(lapply(tables, function(x)x[[2]]))
sumtab <- data.frame(Comparison = contrasts, `Number of genes with adjusted P < 0.05` = sigps,
                     check.names = F)
kable(sumtab, align = 'c') %>% kable_styling() %>%
  add_header_above(c("Overview of results" = 2), font_size = "larger", bold = T, align = "l")
```

# Heatmaps

```{r readtwo-way, results='hide', warning=FALSE, include=FALSE,warning=FALSE}
condition_stats <- read.csv("~/Documents/UCDavis/Whitehead/15_ppt_v_0.2_ppt.csv",stringsAsFactors = FALSE, header = TRUE, row.names = NULL)
M <- read.csv("~/Documents/UCDavis/Whitehead/15_ppt_v_0.2_ppt_M.csv",stringsAsFactors = FALSE, header = TRUE, row.names = NULL)
FW <- read.csv("~/Documents/UCDavis/Whitehead/15_ppt_v_0.2_ppt_FW.csv", stringsAsFactors = FALSE, header = TRUE, row.names = NULL)
Clade3_M <- read.csv("~/Documents/UCDavis/Whitehead/15_ppt_v_0.2_ppt_Clade3_M.csv", stringsAsFactors = FALSE, header = TRUE, row.names = NULL)
Clade3_FW <- read.csv("~/Documents/UCDavis/Whitehead/15_ppt_v_0.2_ppt_Clade3_FW.csv", stringsAsFactors = FALSE, header = TRUE, row.names = NULL)
Clade2_M <- read.csv("~/Documents/UCDavis/Whitehead/15_ppt_v_0.2_ppt_Clade2_M.csv",stringsAsFactors = FALSE, header = TRUE, row.names = NULL)
Clade2_FW <- read.csv("~/Documents/UCDavis/Whitehead/15_ppt_v_0.2_ppt_Clade2_FW.csv", stringsAsFactors = FALSE, header = TRUE, row.names = NULL)
Clade1_M <- read.csv("~/Documents/UCDavis/Whitehead/15_ppt_v_0.2_ppt_Clade1_M.csv", stringsAsFactors = FALSE, header = TRUE, row.names = NULL)
Clade1_FW <- read.csv("~/Documents/UCDavis/Whitehead/15_ppt_v_0.2_ppt_Clade1_FW.csv", stringsAsFactors = FALSE, header = TRUE, row.names = NULL)
# sig genes 15ppt vs. 0.2ppt
dim(condition_stats)
condition_sig<-condition_stats[condition_stats$adj.P.Val <= 0.05,]
condition_sig<-condition_sig[!is.na(condition_sig$adj.P.Val),]
condition_sig<-condition_sig$X
length(condition_sig)
# sig genes M and FW
# two-way
# conserved response
#dim(M)
M_sig <- M[M$adj.P.Val <= 0.05,] 
M_sig <- M_sig[!is.na(M_sig$adj.P.Val),]
M_sig <- M_sig$X
#length(M_sig)
#dim(FW)
FW_sig <- FW[FW$adj.P.Val <= 0.05,] 
FW_sig <- FW_sig[!is.na(FW_sig$adj.P.Val),]
FW_sig <- FW_sig$X
#length(FW_sig)
```

```{r readthreeway, results='hide', include=FALSE,warning=FALSE}
# Clade 3 - specific response
# sig genes Clade 3, M
# but not sig for 2-way

Clade3_M_sig <- Clade3_M[Clade3_M$adj.P.Val <= 0.05,]
Clade3_M_sig <- Clade3_M_sig[!is.na(Clade3_M_sig$adj.P.Val),]
dim(Clade3_M_sig)
Clade3_M_sig_specific <- Clade3_M_sig[!Clade3_M_sig$X %in% M_sig,]
dim(Clade3_M_sig_specific)
Clade3_M_sig_specific <- Clade3_M_sig_specific$X

# Clade 3 - specific response
# sig genes Clade 1,FW
# but not sig for 2-way

Clade3_FW_sig <- Clade3_FW[Clade3_FW$adj.P.Val <= 0.05,]
Clade3_FW_sig <- Clade3_FW_sig[!is.na(Clade3_FW_sig$adj.P.Val),]
dim(Clade3_FW_sig)
Clade3_FW_sig_specific <- Clade3_FW_sig[!Clade3_FW_sig$X %in% FW_sig,]
dim(Clade3_FW_sig_specific)
Clade3_FW_sig_specific <- Clade3_FW_sig_specific$X

# Clade 2 - specific response
# sig genes Clade 2, M
# but not sig for 2-way

Clade2_M_sig <- Clade2_M[Clade2_M$adj.P.Val <= 0.05,]
Clade2_M_sig <- Clade2_M_sig[!is.na(Clade2_M_sig$adj.P.Val),]
dim(Clade2_M_sig)
Clade2_M_sig_specific <- Clade2_M_sig[!Clade2_M_sig$X %in% M_sig,]
dim(Clade2_M_sig_specific)
Clade2_M_sig_specific <- Clade2_M_sig_specific$X

# Clade 2 - specific response
# sig genes Clade 2, FW
# but not sig for 2-way

Clade2_FW_sig <- Clade2_FW[Clade2_FW$adj.P.Val <= 0.05,]
Clade2_FW_sig <- Clade2_FW_sig[!is.na(Clade2_FW_sig$adj.P.Val),]
dim(Clade2_FW_sig)
Clade2_FW_sig_specific <- Clade2_FW_sig[!Clade2_FW_sig$X %in% FW_sig,]
dim(Clade2_FW_sig_specific)
Clade2_FW_sig_specific <- Clade2_FW_sig_specific$X

# Clade 1 - specific response
# sig genes Clade 1, M
# but not sig for 2-way

Clade1_M_sig <- Clade1_M[Clade1_M$adj.P.Val <= 0.05,]
Clade1_M_sig <- Clade1_M_sig[!is.na(Clade1_M_sig$adj.P.Val),]
dim(Clade1_M_sig)
Clade1_M_sig_specific <- Clade1_M_sig[!Clade1_M_sig$X %in% M_sig,]
dim(Clade1_M_sig_specific)
Clade1_M_sig_specific <- Clade1_M_sig_specific$X

# Clade 1 - specific response
# sig genes Clade 1, FW
# but not sig for 2-way

Clade1_FW_sig <- Clade1_FW[Clade1_FW$adj.P.Val <= 0.05,]
Clade1_FW_sig <- Clade1_FW_sig[!is.na(Clade1_FW_sig$adj.P.Val),]
dim(Clade1_FW_sig)
Clade1_FW_sig_specific <- Clade1_FW_sig[!Clade1_FW_sig$X %in% FW_sig,]
dim(Clade1_FW_sig_specific)
Clade1_FW_sig_specific <- Clade1_FW_sig_specific$X
```

```{r readnormcounts, results='hide', include=FALSE,warning=FALSE}
norm_counts <- read.csv("~/Documents/UCDavis/Whitehead/normalized_counts.csv",stringsAsFactors = FALSE, header = TRUE, row.names = NULL)
#colnames(norm_counts)
cols.norm_counts <- colnames(norm_counts)
species_condition<-as.vector(paste(species,condition,sep="."))
rownames(norm_counts) <- norm_counts$Ensembl
norm_counts <- norm_counts[,-1]
colnames(norm_counts) <- species_condition
dim(norm_counts) 
```

```{r means, results='hide', include=FALSE,warning=FALSE}
mean_norm_counts<-sapply(unique(colnames(norm_counts)), function(i)
  rowMeans(norm_counts[,colnames(norm_counts) == i]))
ph <- c("M","M","M","M","M","M","M","M","M","M","FW","FW","FW","FW","M","M","M","M","FW","FW","M","M","M","M","FW","FW","FW","FW")
cl <- c("Clade1","Clade1","Clade1","Clade1","Clade1","Clade1","Clade1","Clade1","Clade1","Clade1","Clade1","Clade1","Clade1","Clade1","Clade2","Clade2","Clade2","Clade2","Clade2","Clade2","Clade3","Clade3","Clade3","Clade3","Clade3","Clade3","Clade3","Clade3")
condition <- c("15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt","15_ppt","0.2_ppt")
  
colnames(mean_norm_counts)
sample_order <- c("F_grandis.15_ppt","F_grandis.0.2_ppt","F_similis.15_ppt","F_similis.0.2_ppt",
           "F_diaphanus.15_ppt","F_diaphanus.0.2_ppt","F_heteroclitusMDPL.15_ppt","F_heteroclitusMDPL.0.2_ppt",
           "F_heteroclitusMDPP.15_ppt","F_heteroclitusMDPP.0.2_ppt","F_catanatus.15_ppt","F_catanatus.0.2_ppt",
           "F_rathbuni.15_ppt","F_rathbuni.0.2_ppt","F_parvapinis.15_ppt","F_parvapinis.0.2_ppt",
           "L_parva.15_ppt","L_parva.0.2_ppt","L_goodei.15_ppt","L_goodei.0.2_ppt",
           "F_chrysotus.15_ppt","F_chrysotus.0.2_ppt","A_xenica.15_ppt","A_xenica.0.2_ppt",
           "F_notatus.15_ppt","F_notatus.0.2_ppt","F_olivaceous.15_ppt","F_olivaceous.0.2_ppt")

mean_norm_counts_ordered <- mean_norm_counts[,sample_order]

```


```{r meantwoway, results='hide', include=FALSE,warning=FALSE}
mean_norm_counts_ordered_condition_sig <- mean_norm_counts_ordered[rownames(mean_norm_counts_ordered) %in% condition_sig,]
dim(mean_norm_counts_ordered_condition_sig)
mean_norm_counts_ordered_M_sig <- mean_norm_counts_ordered[rownames(mean_norm_counts_ordered) %in% Clade1_M_sig_specific,]
mean_norm_counts_ordered_FW_sig <- mean_norm_counts_ordered[rownames(mean_norm_counts_ordered) %in% FW_sig,]
mean_norm_counts_ordered_M_Clade1_sig <- mean_norm_counts_ordered[rownames(mean_norm_counts_ordered) %in% Clade1_M_sig_specific,]
mean_norm_counts_ordered_FW_Clade1_sig <- mean_norm_counts_ordered[rownames(mean_norm_counts_ordered) %in% Clade1_FW_sig_specific ,]
mean_norm_counts_ordered_M_Clade2_sig <- mean_norm_counts_ordered[rownames(mean_norm_counts_ordered) %in% Clade2_M_sig_specific ,]
mean_norm_counts_ordered_FW_Clade2_sig <- mean_norm_counts_ordered[rownames(mean_norm_counts_ordered) %in% Clade2_FW_sig_specific ,]
mean_norm_counts_ordered_M_Clade3_sig <- mean_norm_counts_ordered[rownames(mean_norm_counts_ordered) %in% Clade3_M_sig_specific,]
mean_norm_counts_ordered_FW_Clade3_sig <- mean_norm_counts_ordered[rownames(mean_norm_counts_ordered) %in% Clade3_FW_sig_specific ,]
```

## Marine-Clade 1 (three-way) response

```{r MClade1, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
rld <- log2(mean_norm_counts_ordered_M_Clade1_sig+1)
geneDists <- dist(mean_norm_counts_ordered_M_Clade1_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
         clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
```

## Freshwater-Clade 1 (three-way) response

```{r FWClade1, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
rld <- log2(mean_norm_counts_ordered_FW_Clade1_sig+1)
geneDists <- dist(mean_norm_counts_ordered_FW_Clade1_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
         clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
```

## Marine-Clade 2 (three-way) response

```{r MClade2, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
rld <- log2(mean_norm_counts_ordered_M_Clade2_sig+1)
geneDists <- dist(mean_norm_counts_ordered_M_Clade2_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
         clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
```

## Freshwater-Clade 2 (three-way) response

```{r FWClade2, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
rld <- log2(mean_norm_counts_ordered_FW_Clade2_sig+1)
geneDists <- dist(mean_norm_counts_ordered_FW_Clade2_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
         clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
```

## Marine-Clade 3 (three-way) response

```{r MClade3, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
rld <- log2(mean_norm_counts_ordered_M_Clade3_sig+1)
geneDists <- dist(mean_norm_counts_ordered_M_Clade3_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
         clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
```

## Freshwater-Clade 3 (three-way) response

```{r FWClade3, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
rld <- log2(mean_norm_counts_ordered_FW_Clade3_sig+1)
geneDists <- dist(mean_norm_counts_ordered_FW_Clade3_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
         clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
```

### Marine physiology (two-way) conserved response

```{r Mheatmap, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
rld <- log2(mean_norm_counts_ordered_M_sig+1)
geneDists <- dist(mean_norm_counts_ordered_M_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
         clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
```

### Freshwater physiology (two-way) conserved response

```{r FWheatmap, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
rld <- log2(mean_norm_counts_ordered_FW_sig+1)
geneDists <- dist(mean_norm_counts_ordered_FW_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
         clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
```

### Condition response (main effect)

```{r conditionheatmap, fig.keep="last", fig.width=11, fig.path='figures/', dev=c('png', 'pdf'),warning=FALSE}
rld <- log2(mean_norm_counts_ordered_condition_sig+1)
geneDists <- dist(mean_norm_counts_ordered_condition_sig)
df <- data.frame(ph,cl, condition,stringsAsFactors=FALSE)
rownames(df) <- colnames(rld)
pheatmap(rld, show_rownames=FALSE,
         clustering_distance_rows = geneDists, cluster_cols= FALSE,annotation_col=df)
```

```{r packages}

sessionInfo()

```